Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations13007
Missing cells62413
Missing cells (%)17.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory216.0 B

Variable types

Numeric15
Text5
Categorical3
DateTime4

Alerts

10 km Miejsce Open is highly overall correlated with 10 km Tempo and 10 other fieldsHigh correlation
10 km Tempo is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
15 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
15 km Tempo is highly overall correlated with 10 km Miejsce Open and 12 other fieldsHigh correlation
20 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
20 km Tempo is highly overall correlated with 10 km Miejsce Open and 12 other fieldsHigh correlation
5 km Miejsce Open is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
5 km Tempo is highly overall correlated with 10 km Miejsce Open and 10 other fieldsHigh correlation
Kategoria wiekowa is highly overall correlated with Płeć and 1 other fieldsHigh correlation
Kategoria wiekowa Miejsce is highly overall correlated with 15 km Miejsce Open and 6 other fieldsHigh correlation
Miejsce is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
Numer startowy is highly overall correlated with 10 km Miejsce Open and 9 other fieldsHigh correlation
Płeć is highly overall correlated with Kategoria wiekowa and 1 other fieldsHigh correlation
Płeć Miejsce is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
Rocznik is highly overall correlated with Kategoria wiekowaHigh correlation
Tempo is highly overall correlated with 10 km Miejsce Open and 11 other fieldsHigh correlation
Tempo Stabilność is highly overall correlated with 15 km Tempo and 1 other fieldsHigh correlation
Kraj is highly imbalanced (95.9%) Imbalance
Miejsce has 2707 (20.8%) missing values Missing
Miasto has 3094 (23.8%) missing values Missing
Kraj has 2707 (20.8%) missing values Missing
Drużyna has 8026 (61.7%) missing values Missing
Płeć Miejsce has 2707 (20.8%) missing values Missing
Kategoria wiekowa Miejsce has 2718 (20.9%) missing values Missing
Rocznik has 284 (2.2%) missing values Missing
5 km Czas has 2719 (20.9%) missing values Missing
5 km Miejsce Open has 2719 (20.9%) missing values Missing
5 km Tempo has 2719 (20.9%) missing values Missing
10 km Czas has 2719 (20.9%) missing values Missing
10 km Miejsce Open has 2719 (20.9%) missing values Missing
10 km Tempo has 2728 (21.0%) missing values Missing
15 km Czas has 2720 (20.9%) missing values Missing
15 km Miejsce Open has 2720 (20.9%) missing values Missing
15 km Tempo has 2730 (21.0%) missing values Missing
20 km Czas has 2712 (20.9%) missing values Missing
20 km Miejsce Open has 2712 (20.9%) missing values Missing
20 km Tempo has 2722 (20.9%) missing values Missing
Tempo Stabilność has 2740 (21.1%) missing values Missing
Czas has 2055 (15.8%) missing values Missing
Tempo has 2707 (20.8%) missing values Missing
Rocznik is highly skewed (γ1 = -24.52526325) Skewed
Miejsce is uniformly distributed Uniform
5 km Miejsce Open is uniformly distributed Uniform
10 km Miejsce Open is uniformly distributed Uniform
15 km Miejsce Open is uniformly distributed Uniform
20 km Miejsce Open is uniformly distributed Uniform
Numer startowy has unique values Unique

Reproduction

Analysis started2025-06-29 08:17:08.489895
Analysis finished2025-06-29 08:17:36.733989
Duration28.24 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Miejsce
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct10300
Distinct (%)100.0%
Missing2707
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean5150.5803
Minimum1
Maximum10302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:36.899494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile515.95
Q12575.75
median5150.5
Q37725.25
95-th percentile9785.05
Maximum10302
Range10301
Interquartile range (IQR)5149.5

Descriptive statistics

Standard deviation2973.6316
Coefficient of variation (CV)0.57733914
Kurtosis-1.199827
Mean5150.5803
Median Absolute Deviation (MAD)2575
Skewness0.0001426156
Sum53050977
Variance8842484.8
MonotonicityStrictly increasing
2025-06-29T10:17:37.029149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1
 
< 0.1%
10272 1
 
< 0.1%
29 1
 
< 0.1%
10288 1
 
< 0.1%
10287 1
 
< 0.1%
14 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
Other values (10290) 10290
79.1%
(Missing) 2707
 
20.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10302 1
< 0.1%
10301 1
< 0.1%
10300 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%
10296 1
< 0.1%
10295 1
< 0.1%
10294 1
< 0.1%
10293 1
< 0.1%

Numer startowy
Real number (ℝ)

High correlation  Unique 

Distinct13007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12139.714
Minimum1
Maximum86990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:37.144811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile680.3
Q13466
median7045
Q310693.5
95-th percentile76476.5
Maximum86990
Range86989
Interquartile range (IQR)7227.5

Descriptive statistics

Standard deviation18040.402
Coefficient of variation (CV)1.4860649
Kurtosis9.0441874
Mean12139.714
Median Absolute Deviation (MAD)3605
Skewness3.0878585
Sum1.5790126 × 108
Variance3.2545612 × 108
MonotonicityNot monotonic
2025-06-29T10:17:37.301391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80025 1
 
< 0.1%
596 1
 
< 0.1%
616 1
 
< 0.1%
154 1
 
< 0.1%
591 1
 
< 0.1%
521 1
 
< 0.1%
342 1
 
< 0.1%
234 1
 
< 0.1%
455 1
 
< 0.1%
568 1
 
< 0.1%
Other values (12997) 12997
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
86990 1
< 0.1%
86970 1
< 0.1%
86959 1
< 0.1%
86946 1
< 0.1%
86938 1
< 0.1%
86937 1
< 0.1%
86936 1
< 0.1%
86931 1
< 0.1%
86927 1
< 0.1%
86926 1
< 0.1%

Imię
Text

Distinct763
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:37.621651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length6.5552395
Min length1

Characters and Unicode

Total characters85264
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)3.1%

Sample

1st rowNIKODEM
2nd rowMATEUSZ
3rd rowPATRYK
4th rowDARIUSZ
5th rowSZYMON
ValueCountFrequency (%)
tomasz 470
 
3.6%
anonimowy 435
 
3.3%
piotr 402
 
3.1%
paweł 388
 
3.0%
michał 383
 
2.9%
marcin 379
 
2.9%
krzysztof 335
 
2.6%
łukasz 313
 
2.4%
mateusz 289
 
2.2%
jakub 277
 
2.1%
Other values (747) 9366
71.8%
2025-06-29T10:17:38.109786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15546
18.2%
R 6158
 
7.2%
I 5831
 
6.8%
N 5008
 
5.9%
E 4621
 
5.4%
M 4508
 
5.3%
O 4458
 
5.2%
Z 4309
 
5.1%
T 3850
 
4.5%
S 3840
 
4.5%
Other values (33) 27135
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 15546
18.2%
R 6158
 
7.2%
I 5831
 
6.8%
N 5008
 
5.9%
E 4621
 
5.4%
M 4508
 
5.3%
O 4458
 
5.2%
Z 4309
 
5.1%
T 3850
 
4.5%
S 3840
 
4.5%
Other values (33) 27135
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 15546
18.2%
R 6158
 
7.2%
I 5831
 
6.8%
N 5008
 
5.9%
E 4621
 
5.4%
M 4508
 
5.3%
O 4458
 
5.2%
Z 4309
 
5.1%
T 3850
 
4.5%
S 3840
 
4.5%
Other values (33) 27135
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 15546
18.2%
R 6158
 
7.2%
I 5831
 
6.8%
N 5008
 
5.9%
E 4621
 
5.4%
M 4508
 
5.3%
O 4458
 
5.2%
Z 4309
 
5.1%
T 3850
 
4.5%
S 3840
 
4.5%
Other values (33) 27135
31.8%
Distinct8138
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:38.381939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length7.840778
Min length2

Characters and Unicode

Total characters101985
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6182 ?
Unique (%)47.5%

Sample

1st rowDWORCZAK
2nd rowKACZOR
3rd rowKOZŁOWSKI
4th rowBORATYŃSKI
5th rowDOROŻYŃSKI
ValueCountFrequency (%)
zawodnik 453
 
3.5%
nowak 51
 
0.4%
wójcik 37
 
0.3%
kowalczyk 37
 
0.3%
mazur 34
 
0.3%
kaczmarek 32
 
0.2%
kowalski 26
 
0.2%
woźniak 22
 
0.2%
adamczyk 20
 
0.2%
wilk 18
 
0.1%
Other values (8137) 12333
94.4%
2025-06-29T10:17:38.883846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 11388
 
11.2%
A 11219
 
11.0%
I 9313
 
9.1%
S 7075
 
6.9%
O 6634
 
6.5%
Z 5768
 
5.7%
E 5258
 
5.2%
R 5155
 
5.1%
W 5126
 
5.0%
C 4704
 
4.6%
Other values (38) 30345
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 11388
 
11.2%
A 11219
 
11.0%
I 9313
 
9.1%
S 7075
 
6.9%
O 6634
 
6.5%
Z 5768
 
5.7%
E 5258
 
5.2%
R 5155
 
5.1%
W 5126
 
5.0%
C 4704
 
4.6%
Other values (38) 30345
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 11388
 
11.2%
A 11219
 
11.0%
I 9313
 
9.1%
S 7075
 
6.9%
O 6634
 
6.5%
Z 5768
 
5.7%
E 5258
 
5.2%
R 5155
 
5.1%
W 5126
 
5.0%
C 4704
 
4.6%
Other values (38) 30345
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 11388
 
11.2%
A 11219
 
11.0%
I 9313
 
9.1%
S 7075
 
6.9%
O 6634
 
6.5%
Z 5768
 
5.7%
E 5258
 
5.2%
R 5155
 
5.1%
W 5126
 
5.0%
C 4704
 
4.6%
Other values (38) 30345
29.8%

Miasto
Text

Missing 

Distinct1658
Distinct (%)16.7%
Missing3094
Missing (%)23.8%
Memory size101.7 KiB
2025-06-29T10:17:39.278683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length29
Mean length8.0328861
Min length1

Characters and Unicode

Total characters79630
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique952 ?
Unique (%)9.6%

Sample

1st rowKOŚCIAN
2nd rowRADOM
3rd rowRADOM
4th rowWROCŁAW
5th rowLUBON
ValueCountFrequency (%)
wrocław 3526
32.4%
warszawa 292
 
2.7%
poznań 179
 
1.6%
kraków 163
 
1.5%
góra 102
 
0.9%
legnica 84
 
0.8%
oleśnica 83
 
0.8%
opole 78
 
0.7%
lubin 71
 
0.7%
oława 66
 
0.6%
Other values (1706) 6231
57.3%
2025-06-29T10:17:39.650263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 10835
13.6%
A 8754
11.0%
O 7635
 
9.6%
R 6474
 
8.1%
C 6463
 
8.1%
Ł 4597
 
5.8%
I 4335
 
5.4%
E 3765
 
4.7%
Z 3146
 
4.0%
K 2882
 
3.6%
Other values (45) 20744
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 10835
13.6%
A 8754
11.0%
O 7635
 
9.6%
R 6474
 
8.1%
C 6463
 
8.1%
Ł 4597
 
5.8%
I 4335
 
5.4%
E 3765
 
4.7%
Z 3146
 
4.0%
K 2882
 
3.6%
Other values (45) 20744
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 10835
13.6%
A 8754
11.0%
O 7635
 
9.6%
R 6474
 
8.1%
C 6463
 
8.1%
Ł 4597
 
5.8%
I 4335
 
5.4%
E 3765
 
4.7%
Z 3146
 
4.0%
K 2882
 
3.6%
Other values (45) 20744
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 10835
13.6%
A 8754
11.0%
O 7635
 
9.6%
R 6474
 
8.1%
C 6463
 
8.1%
Ł 4597
 
5.8%
I 4335
 
5.4%
E 3765
 
4.7%
Z 3146
 
4.0%
K 2882
 
3.6%
Other values (45) 20744
26.1%

Kraj
Categorical

Imbalance  Missing 

Distinct36
Distinct (%)0.3%
Missing2707
Missing (%)20.8%
Memory size101.7 KiB
POL
10108 
GER
 
44
GBR
 
19
UKR
 
18
ESP
 
12
Other values (31)
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30900
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.2%

Sample

1st rowPOL
2nd rowPOL
3rd rowPOL
4th rowPOL
5th rowPOL

Common Values

ValueCountFrequency (%)
POL 10108
77.7%
GER 44
 
0.3%
GBR 19
 
0.1%
UKR 18
 
0.1%
ESP 12
 
0.1%
AUT 11
 
0.1%
CZE 10
 
0.1%
BLR 9
 
0.1%
ITA 8
 
0.1%
GRE 8
 
0.1%
Other values (26) 53
 
0.4%
(Missing) 2707
 
20.8%

Length

2025-06-29T10:17:39.765968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pol 10108
98.1%
ger 44
 
0.4%
gbr 19
 
0.2%
ukr 18
 
0.2%
esp 12
 
0.1%
aut 11
 
0.1%
cze 10
 
0.1%
blr 9
 
0.1%
ita 8
 
0.1%
gre 8
 
0.1%
Other values (26) 53
 
0.5%

Most occurring characters

ValueCountFrequency (%)
L 10126
32.8%
P 10123
32.8%
O 10112
32.7%
R 113
 
0.4%
E 95
 
0.3%
G 81
 
0.3%
U 36
 
0.1%
B 33
 
0.1%
A 32
 
0.1%
S 28
 
0.1%
Other values (12) 121
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 10126
32.8%
P 10123
32.8%
O 10112
32.7%
R 113
 
0.4%
E 95
 
0.3%
G 81
 
0.3%
U 36
 
0.1%
B 33
 
0.1%
A 32
 
0.1%
S 28
 
0.1%
Other values (12) 121
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 10126
32.8%
P 10123
32.8%
O 10112
32.7%
R 113
 
0.4%
E 95
 
0.3%
G 81
 
0.3%
U 36
 
0.1%
B 33
 
0.1%
A 32
 
0.1%
S 28
 
0.1%
Other values (12) 121
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 10126
32.8%
P 10123
32.8%
O 10112
32.7%
R 113
 
0.4%
E 95
 
0.3%
G 81
 
0.3%
U 36
 
0.1%
B 33
 
0.1%
A 32
 
0.1%
S 28
 
0.1%
Other values (12) 121
 
0.4%

Drużyna
Text

Missing 

Distinct2745
Distinct (%)55.1%
Missing8026
Missing (%)61.7%
Memory size101.7 KiB
2025-06-29T10:17:39.974206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length76
Median length46
Mean length14.061032
Min length1

Characters and Unicode

Total characters70038
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1968 ?
Unique (%)39.5%

Sample

1st rowRLTL OPTIMA RADOM
2nd rowRLTL-ZTE-RADOM
3rd rowWOSIEK TEAM AZS AWF WROCŁAW
4th rowSZYMI TEAM AZS POLITECHNIKA OPOLSKA
5th rowSZYMON GUMKOWSKI TEAM RUN RADICAL
ValueCountFrequency (%)
team 795
 
7.4%
brak 202
 
1.9%
wrocław 197
 
1.8%
running 188
 
1.8%
188
 
1.8%
biega 133
 
1.2%
runners 118
 
1.1%
run 106
 
1.0%
kb 94
 
0.9%
grupa 79
 
0.7%
Other values (3298) 8610
80.4%
2025-06-29T10:17:40.409024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5748
 
8.2%
5729
 
8.2%
E 4346
 
6.2%
I 3810
 
5.4%
R 3614
 
5.2%
O 3271
 
4.7%
N 3203
 
4.6%
T 2778
 
4.0%
S 2665
 
3.8%
K 2481
 
3.5%
Other values (92) 32393
46.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5748
 
8.2%
5729
 
8.2%
E 4346
 
6.2%
I 3810
 
5.4%
R 3614
 
5.2%
O 3271
 
4.7%
N 3203
 
4.6%
T 2778
 
4.0%
S 2665
 
3.8%
K 2481
 
3.5%
Other values (92) 32393
46.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5748
 
8.2%
5729
 
8.2%
E 4346
 
6.2%
I 3810
 
5.4%
R 3614
 
5.2%
O 3271
 
4.7%
N 3203
 
4.6%
T 2778
 
4.0%
S 2665
 
3.8%
K 2481
 
3.5%
Other values (92) 32393
46.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5748
 
8.2%
5729
 
8.2%
E 4346
 
6.2%
I 3810
 
5.4%
R 3614
 
5.2%
O 3271
 
4.7%
N 3203
 
4.6%
T 2778
 
4.0%
S 2665
 
3.8%
K 2481
 
3.5%
Other values (92) 32393
46.3%

Płeć
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing9
Missing (%)0.1%
Memory size101.7 KiB
M
9004 
K
3994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12998
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 9004
69.2%
K 3994
30.7%
(Missing) 9
 
0.1%

Length

2025-06-29T10:17:40.521178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-29T10:17:40.593191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 9004
69.3%
k 3994
30.7%

Most occurring characters

ValueCountFrequency (%)
M 9004
69.3%
K 3994
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 9004
69.3%
K 3994
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 9004
69.3%
K 3994
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 9004
69.3%
K 3994
30.7%

Płeć Miejsce
Real number (ℝ)

High correlation  Missing 

Distinct7238
Distinct (%)70.3%
Missing2707
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean2998.8129
Minimum1
Maximum7240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:40.676233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile258
Q11288
median2575.5
Q34663.25
95-th percentile6723.05
Maximum7240
Range7239
Interquartile range (IQR)3375.25

Descriptive statistics

Standard deviation2052.226
Coefficient of variation (CV)0.68434611
Kurtosis-0.96882639
Mean2998.8129
Median Absolute Deviation (MAD)1554.5
Skewness0.45377052
Sum30887773
Variance4211631.4
MonotonicityNot monotonic
2025-06-29T10:17:40.809808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
3046 2
 
< 0.1%
39 2
 
< 0.1%
23 2
 
< 0.1%
1 2
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
Other values (7228) 10280
79.0%
(Missing) 2707
 
20.8%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
7240 1
< 0.1%
7239 1
< 0.1%
7238 1
< 0.1%
7237 1
< 0.1%
7236 1
< 0.1%
7235 1
< 0.1%
7234 1
< 0.1%
7233 1
< 0.1%
7232 1
< 0.1%
7231 1
< 0.1%

Kategoria wiekowa
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing20
Missing (%)0.2%
Memory size101.7 KiB
M40
2969 
M30
2921 
M20
1658 
K30
1415 
K40
1320 
Other values (8)
2704 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters38961
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM20
2nd rowM20
3rd rowM20
4th rowM20
5th rowM30

Common Values

ValueCountFrequency (%)
M40 2969
22.8%
M30 2921
22.5%
M20 1658
12.7%
K30 1415
10.9%
K40 1320
10.1%
M50 1016
 
7.8%
K20 880
 
6.8%
M60 357
 
2.7%
K50 299
 
2.3%
M70 70
 
0.5%
Other values (3) 82
 
0.6%

Length

2025-06-29T10:17:41.040434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m40 2969
22.9%
m30 2921
22.5%
m20 1658
12.8%
k30 1415
10.9%
k40 1320
10.2%
m50 1016
 
7.8%
k20 880
 
6.8%
m60 357
 
2.7%
k50 299
 
2.3%
m70 70
 
0.5%
Other values (3) 82
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 12987
33.3%
M 8994
23.1%
3 4336
 
11.1%
4 4289
 
11.0%
K 3993
 
10.2%
2 2538
 
6.5%
5 1315
 
3.4%
6 423
 
1.1%
7 83
 
0.2%
8 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12987
33.3%
M 8994
23.1%
3 4336
 
11.1%
4 4289
 
11.0%
K 3993
 
10.2%
2 2538
 
6.5%
5 1315
 
3.4%
6 423
 
1.1%
7 83
 
0.2%
8 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12987
33.3%
M 8994
23.1%
3 4336
 
11.1%
4 4289
 
11.0%
K 3993
 
10.2%
2 2538
 
6.5%
5 1315
 
3.4%
6 423
 
1.1%
7 83
 
0.2%
8 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12987
33.3%
M 8994
23.1%
3 4336
 
11.1%
4 4289
 
11.0%
K 3993
 
10.2%
2 2538
 
6.5%
5 1315
 
3.4%
6 423
 
1.1%
7 83
 
0.2%
8 3
 
< 0.1%

Kategoria wiekowa Miejsce
Real number (ℝ)

High correlation  Missing 

Distinct2387
Distinct (%)23.2%
Missing2718
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean801.82603
Minimum1
Maximum2388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:41.138891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47
Q1284
median651
Q31176
95-th percentile2100.6
Maximum2388
Range2387
Interquartile range (IQR)892

Descriptive statistics

Standard deviation629.60511
Coefficient of variation (CV)0.7852141
Kurtosis-0.34736077
Mean801.82603
Median Absolute Deviation (MAD)412
Skewness0.79595083
Sum8249988
Variance396402.59
MonotonicityNot monotonic
2025-06-29T10:17:41.264536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
0.1%
4 12
 
0.1%
3 12
 
0.1%
5 12
 
0.1%
2 12
 
0.1%
6 12
 
0.1%
38 11
 
0.1%
40 11
 
0.1%
41 11
 
0.1%
39 11
 
0.1%
Other values (2377) 10172
78.2%
(Missing) 2718
 
20.9%
ValueCountFrequency (%)
1 13
0.1%
2 12
0.1%
3 12
0.1%
4 12
0.1%
5 12
0.1%
6 12
0.1%
7 11
0.1%
8 11
0.1%
9 11
0.1%
10 11
0.1%
ValueCountFrequency (%)
2388 1
< 0.1%
2387 1
< 0.1%
2386 1
< 0.1%
2385 1
< 0.1%
2384 1
< 0.1%
2383 1
< 0.1%
2382 1
< 0.1%
2381 1
< 0.1%
2380 1
< 0.1%
2379 1
< 0.1%

Rocznik
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct66
Distinct (%)0.5%
Missing284
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1981.8017
Minimum0
Maximum2006
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:41.379536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1967
Q11978
median1985
Q31993
95-th percentile2001
Maximum2006
Range2006
Interquartile range (IQR)15

Descriptive statistics

Standard deviation79.316604
Coefficient of variation (CV)0.040022472
Kurtosis610.00489
Mean1981.8017
Median Absolute Deviation (MAD)7
Skewness-24.525263
Sum25214463
Variance6291.1236
MonotonicityNot monotonic
2025-06-29T10:17:41.511904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1981 498
 
3.8%
1983 493
 
3.8%
1986 477
 
3.7%
1990 470
 
3.6%
1982 469
 
3.6%
1984 457
 
3.5%
1980 449
 
3.5%
1979 438
 
3.4%
1988 437
 
3.4%
1985 430
 
3.3%
Other values (56) 8105
62.3%
ValueCountFrequency (%)
0 20
0.2%
1934 1
 
< 0.1%
1943 1
 
< 0.1%
1944 1
 
< 0.1%
1945 1
 
< 0.1%
1946 2
 
< 0.1%
1947 1
 
< 0.1%
1948 6
 
< 0.1%
1949 11
0.1%
1950 9
0.1%
ValueCountFrequency (%)
2006 21
 
0.2%
2005 64
 
0.5%
2004 85
 
0.7%
2003 132
1.0%
2002 161
1.2%
2001 206
1.6%
2000 228
1.8%
1999 230
1.8%
1998 304
2.3%
1997 326
2.5%

5 km Czas
Date

Missing 

Distinct1240
Distinct (%)12.1%
Missing2719
Missing (%)20.9%
Memory size101.7 KiB
Minimum2025-06-29 00:00:00
Maximum2025-06-29 00:50:14
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:17:41.642904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:41.782944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

5 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct10288
Distinct (%)100.0%
Missing2719
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean5169.4679
Minimum1
Maximum10353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:41.908776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile521.35
Q12588.75
median5166.5
Q37750.25
95-th percentile9821.65
Maximum10353
Range10352
Interquartile range (IQR)5161.5

Descriptive statistics

Standard deviation2983.0608
Coefficient of variation (CV)0.57705374
Kurtosis-1.1983852
Mean5169.4679
Median Absolute Deviation (MAD)2581
Skewness0.0014309075
Sum53183486
Variance8898651.8
MonotonicityNot monotonic
2025-06-29T10:17:42.034470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 1
 
< 0.1%
3 1
 
< 0.1%
10343 1
 
< 0.1%
10342 1
 
< 0.1%
10217 1
 
< 0.1%
9295 1
 
< 0.1%
10336 1
 
< 0.1%
3772 1
 
< 0.1%
10328 1
 
< 0.1%
9535 1
 
< 0.1%
Other values (10278) 10278
79.0%
(Missing) 2719
 
20.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10353 1
< 0.1%
10352 1
< 0.1%
10350 1
< 0.1%
10345 1
< 0.1%
10344 1
< 0.1%
10343 1
< 0.1%
10342 1
< 0.1%
10341 1
< 0.1%
10340 1
< 0.1%
10339 1
< 0.1%

5 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1240
Distinct (%)12.1%
Missing2719
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean5.6605029
Minimum0
Maximum10.046667
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:42.153060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.34
Q15.0891667
median5.63
Q36.19
95-th percentile7.0766667
Maximum10.046667
Range10.046667
Interquartile range (IQR)1.1008333

Descriptive statistics

Standard deviation0.83190666
Coefficient of variation (CV)0.14696692
Kurtosis0.30632784
Mean5.6605029
Median Absolute Deviation (MAD)0.55
Skewness0.23036709
Sum58235.253
Variance0.6920687
MonotonicityNot monotonic
2025-06-29T10:17:42.285172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.643333333 30
 
0.2%
5.633333333 30
 
0.2%
5.673333333 29
 
0.2%
5.67 28
 
0.2%
5.653333333 27
 
0.2%
5.57 27
 
0.2%
5.626666667 27
 
0.2%
5.63 27
 
0.2%
5.316666667 26
 
0.2%
5.666666667 26
 
0.2%
Other values (1230) 10011
77.0%
(Missing) 2719
 
20.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
3.02 3
< 0.1%
3.023333333 1
 
< 0.1%
3.076666667 1
 
< 0.1%
3.156666667 1
 
< 0.1%
3.253333333 1
 
< 0.1%
3.316666667 1
 
< 0.1%
3.32 1
 
< 0.1%
3.323333333 2
< 0.1%
3.326666667 1
 
< 0.1%
ValueCountFrequency (%)
10.04666667 1
< 0.1%
9.866666667 1
< 0.1%
9.36 1
< 0.1%
8.8 1
< 0.1%
8.7 1
< 0.1%
8.676666667 1
< 0.1%
8.523333333 1
< 0.1%
8.52 1
< 0.1%
8.516666667 1
< 0.1%
8.423333333 1
< 0.1%

10 km Czas
Date

Missing 

Distinct2238
Distinct (%)21.8%
Missing2719
Missing (%)20.9%
Memory size101.7 KiB
Minimum2025-06-29 00:29:42
Maximum2025-06-29 01:43:28
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:17:42.424152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:42.553991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

10 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct10288
Distinct (%)100.0%
Missing2719
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean5160.6388
Minimum1
Maximum10330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:42.681571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile520.35
Q12581.75
median5158.5
Q37739.25
95-th percentile9803.65
Maximum10330
Range10329
Interquartile range (IQR)5157.5

Descriptive statistics

Standard deviation2978.7938
Coefficient of variation (CV)0.57721417
Kurtosis-1.1997935
Mean5160.6388
Median Absolute Deviation (MAD)2579
Skewness0.00085810257
Sum53092652
Variance8873212.7
MonotonicityNot monotonic
2025-06-29T10:17:42.804918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1
 
< 0.1%
3 1
 
< 0.1%
10322 1
 
< 0.1%
10314 1
 
< 0.1%
10279 1
 
< 0.1%
10009 1
 
< 0.1%
10323 1
 
< 0.1%
4221 1
 
< 0.1%
10316 1
 
< 0.1%
10311 1
 
< 0.1%
Other values (10278) 10278
79.0%
(Missing) 2719
 
20.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10330 1
< 0.1%
10329 1
< 0.1%
10328 1
< 0.1%
10324 1
< 0.1%
10323 1
< 0.1%
10322 1
< 0.1%
10321 1
< 0.1%
10320 1
< 0.1%
10319 1
< 0.1%
10318 1
< 0.1%

10 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1328
Distinct (%)12.9%
Missing2728
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean5.5998638
Minimum2.92
Maximum11.346667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:42.932953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.92
5-th percentile4.2466667
Q14.9733333
median5.5066667
Q36.1333333
95-th percentile7.18
Maximum11.346667
Range8.4266667
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation0.90211865
Coefficient of variation (CV)0.16109653
Kurtosis0.77195002
Mean5.5998638
Median Absolute Deviation (MAD)0.58
Skewness0.57319851
Sum57561
Variance0.81381806
MonotonicityNot monotonic
2025-06-29T10:17:43.081953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.366666667 34
 
0.3%
5.36 29
 
0.2%
5.776666667 29
 
0.2%
5.316666667 28
 
0.2%
5.42 27
 
0.2%
5.476666667 26
 
0.2%
5.43 25
 
0.2%
5.296666667 24
 
0.2%
5.486666667 24
 
0.2%
5.426666667 24
 
0.2%
Other values (1318) 10009
77.0%
(Missing) 2728
 
21.0%
ValueCountFrequency (%)
2.92 3
< 0.1%
3.11 1
 
< 0.1%
3.153333333 1
 
< 0.1%
3.233333333 2
< 0.1%
3.25 1
 
< 0.1%
3.266666667 2
< 0.1%
3.27 1
 
< 0.1%
3.296666667 2
< 0.1%
3.323333333 1
 
< 0.1%
3.393333333 1
 
< 0.1%
ValueCountFrequency (%)
11.34666667 1
< 0.1%
10.64666667 1
< 0.1%
10.40666667 1
< 0.1%
10.25 1
< 0.1%
10.13333333 1
< 0.1%
9.87 1
< 0.1%
9.796666667 1
< 0.1%
9.533333333 1
< 0.1%
9.343333333 2
< 0.1%
9.093333333 1
< 0.1%

15 km Czas
Date

Missing 

Distinct3184
Distinct (%)31.0%
Missing2720
Missing (%)20.9%
Memory size101.7 KiB
Minimum2025-06-29 00:45:07
Maximum2025-06-29 02:34:09
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:17:43.194686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:43.333553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

15 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct10287
Distinct (%)100.0%
Missing2720
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean5153.5215
Minimum1
Maximum10305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:43.468579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile519.3
Q12579.5
median5154
Q37727.5
95-th percentile9787.7
Maximum10305
Range10304
Interquartile range (IQR)5148

Descriptive statistics

Standard deviation2973.5187
Coefficient of variation (CV)0.57698772
Kurtosis-1.1998684
Mean5153.5215
Median Absolute Deviation (MAD)2574
Skewness-0.0002362966
Sum53014276
Variance8841813.2
MonotonicityNot monotonic
2025-06-29T10:17:43.600170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10299 1
 
< 0.1%
10298 1
 
< 0.1%
10282 1
 
< 0.1%
10285 1
 
< 0.1%
10287 1
 
< 0.1%
10283 1
 
< 0.1%
10302 1
 
< 0.1%
4570 1
 
< 0.1%
10300 1
 
< 0.1%
10296 1
 
< 0.1%
Other values (10277) 10277
79.0%
(Missing) 2720
 
20.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10305 1
< 0.1%
10304 1
< 0.1%
10303 1
< 0.1%
10302 1
< 0.1%
10301 1
< 0.1%
10300 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%
10296 1
< 0.1%

15 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1479
Distinct (%)14.4%
Missing2730
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean5.9531352
Minimum3.0833333
Maximum11.213333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:43.725017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0833333
5-th percentile4.4733333
Q15.2333333
median5.82
Q36.5633333
95-th percentile7.834
Maximum11.213333
Range8.13
Interquartile range (IQR)1.33

Descriptive statistics

Standard deviation1.0202978
Coefficient of variation (CV)0.17138831
Kurtosis0.4243088
Mean5.9531352
Median Absolute Deviation (MAD)0.65
Skewness0.59317394
Sum61180.37
Variance1.0410075
MonotonicityNot monotonic
2025-06-29T10:17:43.962299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.766666667 26
 
0.2%
5.726666667 26
 
0.2%
5.593333333 25
 
0.2%
5.656666667 25
 
0.2%
5.543333333 25
 
0.2%
5.486666667 25
 
0.2%
5.733333333 24
 
0.2%
5.71 24
 
0.2%
5.466666667 23
 
0.2%
5.663333333 23
 
0.2%
Other values (1469) 10031
77.1%
(Missing) 2730
 
21.0%
ValueCountFrequency (%)
3.083333333 3
< 0.1%
3.293333333 1
 
< 0.1%
3.303333333 1
 
< 0.1%
3.336666667 1
 
< 0.1%
3.363333333 1
 
< 0.1%
3.39 2
< 0.1%
3.406666667 1
 
< 0.1%
3.413333333 1
 
< 0.1%
3.453333333 1
 
< 0.1%
3.486666667 1
 
< 0.1%
ValueCountFrequency (%)
11.21333333 1
< 0.1%
10.44333333 1
< 0.1%
10.13666667 1
< 0.1%
10.11666667 1
< 0.1%
9.98 1
< 0.1%
9.886666667 1
< 0.1%
9.88 1
< 0.1%
9.873333333 1
< 0.1%
9.866666667 1
< 0.1%
9.836666667 1
< 0.1%

20 km Czas
Date

Missing 

Distinct4035
Distinct (%)39.2%
Missing2712
Missing (%)20.9%
Memory size101.7 KiB
Minimum2025-06-29 01:00:33
Maximum2025-06-29 03:21:22
Invalid dates0
Invalid dates (%)0.0%
2025-06-29T10:17:44.085944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:44.242902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

20 km Miejsce Open
Real number (ℝ)

High correlation  Missing  Uniform 

Distinct10295
Distinct (%)100.0%
Missing2712
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean5152.5569
Minimum1
Maximum10306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:44.410026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile515.7
Q12577.5
median5153
Q37727.5
95-th percentile9789.3
Maximum10306
Range10305
Interquartile range (IQR)5150

Descriptive statistics

Standard deviation2974.7583
Coefficient of variation (CV)0.57733634
Kurtosis-1.1996019
Mean5152.5569
Median Absolute Deviation (MAD)2575
Skewness-0.0001563583
Sum53045573
Variance8849187
MonotonicityNot monotonic
2025-06-29T10:17:44.541918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10277 1
 
< 0.1%
25 1
 
< 0.1%
1 1
 
< 0.1%
10292 1
 
< 0.1%
Other values (10285) 10285
79.1%
(Missing) 2712
 
20.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10306 1
< 0.1%
10305 1
< 0.1%
10304 1
< 0.1%
10303 1
< 0.1%
10302 1
< 0.1%
10301 1
< 0.1%
10300 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%

20 km Tempo
Real number (ℝ)

High correlation  Missing 

Distinct1638
Distinct (%)15.9%
Missing2722
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean6.2327924
Minimum3.0866667
Maximum11.883333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:44.656777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0866667
5-th percentile4.5973333
Q15.4066667
median6.04
Q36.9066667
95-th percentile8.44
Maximum11.883333
Range8.7966667
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1678774
Coefficient of variation (CV)0.18737628
Kurtosis0.47396509
Mean6.2327924
Median Absolute Deviation (MAD)0.72666667
Skewness0.71101407
Sum64104.27
Variance1.3639377
MonotonicityNot monotonic
2025-06-29T10:17:44.771400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.406666667 26
 
0.2%
5.733333333 26
 
0.2%
5.68 23
 
0.2%
5.683333333 22
 
0.2%
6.706666667 22
 
0.2%
5.61 22
 
0.2%
6.39 21
 
0.2%
6.016666667 21
 
0.2%
5.55 21
 
0.2%
5.773333333 21
 
0.2%
Other values (1628) 10060
77.3%
(Missing) 2722
 
20.9%
ValueCountFrequency (%)
3.086666667 1
 
< 0.1%
3.103333333 1
 
< 0.1%
3.173333333 1
 
< 0.1%
3.393333333 1
 
< 0.1%
3.44 1
 
< 0.1%
3.486666667 1
 
< 0.1%
3.533333333 2
< 0.1%
3.573333333 1
 
< 0.1%
3.583333333 3
< 0.1%
3.586666667 1
 
< 0.1%
ValueCountFrequency (%)
11.88333333 1
< 0.1%
11.07 1
< 0.1%
10.98 1
< 0.1%
10.97 1
< 0.1%
10.91 1
< 0.1%
10.83 1
< 0.1%
10.8 1
< 0.1%
10.57 1
< 0.1%
10.54 1
< 0.1%
10.48333333 1
< 0.1%

Tempo Stabilność
Real number (ℝ)

High correlation  Missing 

Distinct5263
Distinct (%)51.3%
Missing2740
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean0.041465965
Minimum-0.1278
Maximum0.43613333
Zeros1
Zeros (%)< 0.1%
Negative1076
Negative (%)8.3%
Memory size101.7 KiB
2025-06-29T10:17:44.883576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.1278
5-th percentile-0.010113333
Q10.013
median0.030933333
Q30.061133333
95-th percentile0.12614
Maximum0.43613333
Range0.56393333
Interquartile range (IQR)0.048133333

Descriptive statistics

Standard deviation0.043747281
Coefficient of variation (CV)1.0550166
Kurtosis3.8860708
Mean0.041465965
Median Absolute Deviation (MAD)0.021733333
Skewness1.4168266
Sum425.73107
Variance0.0019138246
MonotonicityNot monotonic
2025-06-29T10:17:45.005115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01713333333 14
 
0.1%
0.01446666667 11
 
0.1%
0.02453333333 10
 
0.1%
0.01393333333 10
 
0.1%
0.01326666667 10
 
0.1%
0.01706666667 10
 
0.1%
0.02466666667 10
 
0.1%
0.008333333333 10
 
0.1%
0.0362 9
 
0.1%
0.02306666667 9
 
0.1%
Other values (5253) 10164
78.1%
(Missing) 2740
 
21.1%
ValueCountFrequency (%)
-0.1278 1
< 0.1%
-0.1023333333 1
< 0.1%
-0.1021333333 1
< 0.1%
-0.102 1
< 0.1%
-0.08766666667 1
< 0.1%
-0.06933333333 1
< 0.1%
-0.069 1
< 0.1%
-0.06753333333 1
< 0.1%
-0.06406666667 1
< 0.1%
-0.06393333333 1
< 0.1%
ValueCountFrequency (%)
0.4361333333 1
< 0.1%
0.4219333333 1
< 0.1%
0.3192666667 1
< 0.1%
0.3005333333 1
< 0.1%
0.2938 1
< 0.1%
0.292 1
< 0.1%
0.2907333333 1
< 0.1%
0.2889333333 1
< 0.1%
0.2837333333 1
< 0.1%
0.2785333333 1
< 0.1%

Czas
Text

Missing 

Distinct4218
Distinct (%)38.5%
Missing2055
Missing (%)15.8%
Memory size101.7 KiB
2025-06-29T10:17:45.231794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.7023375
Min length3

Characters and Unicode

Total characters84356
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1566 ?
Unique (%)14.3%

Sample

1st row01:04:03
2nd row01:04:24
3rd row01:04:40
4th row01:09:44
5th row01:10:05
ValueCountFrequency (%)
dns 570
 
5.2%
dnf 82
 
0.7%
01:55:25 14
 
0.1%
01:59:43 13
 
0.1%
01:58:10 11
 
0.1%
02:10:23 11
 
0.1%
01:59:29 9
 
0.1%
01:55:21 9
 
0.1%
01:53:40 9
 
0.1%
01:58:04 9
 
0.1%
Other values (4208) 10215
93.3%
2025-06-29T10:17:45.552518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 20600
24.4%
0 16062
19.0%
2 10367
12.3%
1 10049
11.9%
5 6089
 
7.2%
4 5745
 
6.8%
3 5001
 
5.9%
9 2248
 
2.7%
8 2100
 
2.5%
7 2078
 
2.5%
Other values (5) 4017
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 20600
24.4%
0 16062
19.0%
2 10367
12.3%
1 10049
11.9%
5 6089
 
7.2%
4 5745
 
6.8%
3 5001
 
5.9%
9 2248
 
2.7%
8 2100
 
2.5%
7 2078
 
2.5%
Other values (5) 4017
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 20600
24.4%
0 16062
19.0%
2 10367
12.3%
1 10049
11.9%
5 6089
 
7.2%
4 5745
 
6.8%
3 5001
 
5.9%
9 2248
 
2.7%
8 2100
 
2.5%
7 2078
 
2.5%
Other values (5) 4017
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 20600
24.4%
0 16062
19.0%
2 10367
12.3%
1 10049
11.9%
5 6089
 
7.2%
4 5745
 
6.8%
3 5001
 
5.9%
9 2248
 
2.7%
8 2100
 
2.5%
7 2078
 
2.5%
Other values (5) 4017
 
4.8%

Tempo
Real number (ℝ)

High correlation  Missing 

Distinct4216
Distinct (%)40.9%
Missing2707
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean5.8896096
Minimum3.0362645
Maximum10.076637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2025-06-29T10:17:45.662036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0362645
5-th percentile4.4528324
Q15.2119381
median5.7719049
Q36.4819863
95-th percentile7.6353401
Maximum10.076637
Range7.0403729
Interquartile range (IQR)1.2700482

Descriptive statistics

Standard deviation0.96023771
Coefficient of variation (CV)0.16303928
Kurtosis0.21444315
Mean5.8896096
Median Absolute Deviation (MAD)0.62337047
Skewness0.47789712
Sum60662.979
Variance0.92205647
MonotonicityIncreasing
2025-06-29T10:17:45.783749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.471280714 14
 
0.1%
5.675120487 13
 
0.1%
6.180769535 11
 
0.1%
5.601643359 11
 
0.1%
5.468120408 9
 
0.1%
5.95164731 9
 
0.1%
5.5969029 9
 
0.1%
5.612704432 9
 
0.1%
5.388322667 9
 
0.1%
5.68618156 9
 
0.1%
Other values (4206) 10197
78.4%
(Missing) 2707
 
20.8%
ValueCountFrequency (%)
3.036264518 1
< 0.1%
3.052856127 1
< 0.1%
3.065497353 1
< 0.1%
3.305680651 1
< 0.1%
3.32227226 1
< 0.1%
3.332543257 1
< 0.1%
3.356245556 1
< 0.1%
3.379157778 1
< 0.1%
3.384688315 2
< 0.1%
3.404440231 1
< 0.1%
ValueCountFrequency (%)
10.07663743 1
< 0.1%
9.885438888 1
< 0.1%
9.589950225 1
< 0.1%
9.534644861 1
< 0.1%
9.470648653 1
< 0.1%
9.37504938 1
< 0.1%
9.300782176 1
< 0.1%
9.298411946 1
< 0.1%
9.261278344 1
< 0.1%
9.256537884 1
< 0.1%

Interactions

2025-06-29T10:17:33.742359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.208346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.651871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.189181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.836315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.451355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.154780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.643751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.133465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.577609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.859698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.401682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.912060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.559322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.138452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.839821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.303026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.826734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.308844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.954806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.567948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.262256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.730196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.234126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.661814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.949836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.494681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.007248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.655709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.276107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.935821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.395540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.924732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.425920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.043453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.665228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.364978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.832768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.327710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.749886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.042979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.588337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.175041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.765986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.372534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.035388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.507753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.033732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.541738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.143902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.773786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.467999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.953979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.422217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.836524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.139509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.687843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.270837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.876850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.485749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.128896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.636037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.127427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.650732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.237319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.875391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.573878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.049404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.536324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.921201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.232043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.786362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.385093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.978051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.580961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.238980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.748602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.235336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.770322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.335024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.000349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.688453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.141153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.634648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.010770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.326902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.888871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.488471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.074054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.687555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.328375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.845111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.322950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.868321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.441529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.135533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.776680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.229154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.727181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.095796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.425328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.985124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.602959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.175718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.777176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.421092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:12.941114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.410952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.964324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.530888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.242199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.871994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.311153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.820003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.182734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.521087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.084704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.701786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.258718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.869866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.516115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.031113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.503856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.076076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.625886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.377147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.991351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.402403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.907001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.261381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.615250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.188981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.790113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.354585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.966937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.603132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.114006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.605013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.172076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.706906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.482312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.085959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.582038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.993487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.337109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.700592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.307981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.882114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.440784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.051974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.716541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.203004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.693734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.270741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.802084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.577368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.178842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.667091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.086408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.419725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.788810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.415546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:29.968683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.653100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.158934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.822942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.295183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.791756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.371827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:17.921086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.684026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.274553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.758277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.178614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.505725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:26.883813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.520055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.069335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.742691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.328858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:34.918808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.384099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.885269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.494080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.168266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.777027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.373789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.845275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.273128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.590152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.121050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.620504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.185128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.831312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.448764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:35.002538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.472106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:14.981857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.609619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.265271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:19.953592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.459821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:22.924629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.366434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.674151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.206059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.710504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.338856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:31.920874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.554357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:35.094082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:13.558846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:15.075095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:16.734530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:18.356958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:20.056027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:21.554226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:23.026998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:24.474948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:25.772192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:27.308205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:28.815545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:30.456064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:32.014207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-29T10:17:33.651646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-29T10:17:45.887507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
10 km Miejsce Open10 km Tempo15 km Miejsce Open15 km Tempo20 km Miejsce Open20 km Tempo5 km Miejsce Open5 km TempoKategoria wiekowaKategoria wiekowa MiejsceKrajMiejsceNumer startowyPłećPłeć MiejsceRocznikTempoTempo Stabilność
10 km Miejsce Open1.0000.9920.9930.9530.9760.8910.9900.9900.1320.4830.0240.9710.7560.3640.602-0.0290.9710.292
10 km Tempo0.9921.0000.9940.9690.9830.9120.9650.9650.1360.4920.0340.9790.7410.3470.611-0.0330.9790.359
15 km Miejsce Open0.9930.9941.0000.9820.9930.9270.9720.9720.1290.5010.0230.9890.7480.3560.623-0.0330.9890.379
15 km Tempo0.9530.9690.9821.0000.9910.9620.9180.9180.1220.5150.0340.9900.7140.3310.638-0.0390.9900.512
20 km Miejsce Open0.9760.9830.9930.9911.0000.9640.9490.9490.1240.5200.0220.9990.7340.3390.645-0.0350.9990.469
20 km Tempo0.8910.9120.9270.9620.9641.0000.8520.8520.1060.5350.0390.9690.6690.2820.661-0.0420.9690.655
5 km Miejsce Open0.9900.9650.9720.9180.9490.8521.0001.0000.1290.4670.0210.9430.7610.3600.583-0.0250.9430.215
5 km Tempo0.9900.9650.9720.9180.9490.8521.0001.0000.1510.4670.0410.9430.7610.3440.583-0.0250.9430.215
Kategoria wiekowa0.1320.1360.1290.1220.1240.1060.1290.1511.0000.2550.0000.1220.0951.0000.1781.0000.1360.043
Kategoria wiekowa Miejsce0.4830.4920.5010.5150.5200.5350.4670.4670.2551.0000.0090.5230.3740.3960.8010.0920.5230.298
Kraj0.0240.0340.0230.0340.0220.0390.0210.0410.0000.0091.0000.0250.0000.0000.0190.0000.0620.161
Miejsce0.9710.9790.9890.9900.9990.9690.9430.9430.1220.5230.0251.0000.7300.3340.649-0.0381.0000.483
Numer startowy0.7560.7410.7480.7140.7340.6690.7610.7610.0950.3740.0000.7301.0000.2240.4460.0430.7300.184
Płeć0.3640.3470.3560.3310.3390.2820.3600.3441.0000.3960.0000.3340.2241.0000.5130.0060.3270.067
Płeć Miejsce0.6020.6110.6230.6380.6450.6610.5830.5830.1780.8010.0190.6490.4460.5131.000-0.0890.6490.376
Rocznik-0.029-0.033-0.033-0.039-0.035-0.042-0.025-0.0251.0000.0920.000-0.0380.0430.006-0.0891.000-0.038-0.061
Tempo0.9710.9790.9890.9900.9990.9690.9430.9430.1360.5230.0621.0000.7300.3270.649-0.0381.0000.483
Tempo Stabilność0.2920.3590.3790.5120.4690.6550.2150.2150.0430.2980.1610.4830.1840.0670.376-0.0610.4831.000

Missing values

2025-06-29T10:17:35.289507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-29T10:17:35.538412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-29T10:17:36.201057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MiejsceNumer startowyImięNazwiskoMiastoKrajDrużynaPłećPłeć MiejsceKategoria wiekowaKategoria wiekowa MiejsceRocznik5 km Czas5 km Miejsce Open5 km Tempo10 km Czas10 km Miejsce Open10 km Tempo15 km Czas15 km Miejsce Open15 km Tempo20 km Czas20 km Miejsce Open20 km TempoTempo StabilnośćCzasTempo
01.0596NIKODEMDWORCZAKKOŚCIANPOLNaNM1.0M201.01998.000:15:063.03.02000000:29:423.02.92000000:45:072.03.08333301:00:331.03.0866670.00726701:04:033.036265
12.0616MATEUSZKACZORRADOMPOLRLTL OPTIMA RADOMM2.0M202.01997.000:15:064.03.02000000:29:421.02.92000000:45:073.03.08333301:00:382.03.1033330.00826701:04:243.052856
23.0154PATRYKKOZŁOWSKIRADOMPOLRLTL-ZTE-RADOMM3.0M203.01998.000:15:062.03.02000000:29:422.02.92000000:45:071.03.08333301:00:593.03.1733330.01246701:04:403.065497
34.0591DARIUSZBORATYŃSKIWROCŁAWPOLWOSIEK TEAM AZS AWF WROCŁAWM4.0M204.01997.000:15:477.03.15666700:31:205.03.11000000:47:484.03.29333301:05:404.03.5733330.02866701:09:443.305681
45.0521SZYMONDOROŻYŃSKILUBONPOLSZYMI TEAM AZS POLITECHNIKA OPOLSKAM5.0M301.01992.000:15:075.03.02333300:30:534.03.15333300:48:095.03.45333301:06:055.03.5866670.03980001:10:053.322272
56.0342SZYMONGUMKOWSKIGDAŃSKPOLSZYMON GUMKOWSKI TEAM RUN RADICALM6.0M205.01999.000:16:359.03.31666700:32:456.03.23333300:49:346.03.36333301:06:326.03.3933330.00720001:10:183.332543
67.0234ADAMPUTYRAWROCŁAWPOLNaNM7.0M401.01983.000:16:4515.03.35000000:33:0612.03.27000000:49:478.03.33666701:06:597.03.4400000.00673301:10:483.356246
78.0455ROMANADAMOVICHLESZNOINNACHILLES LESZNOM8.0M206.01996.000:15:236.03.07666700:33:1614.03.57666700:49:477.03.30333301:07:138.03.4866670.01913301:11:173.379158
89.0568KAMILMAŃKOWSKIWROCŁAWPOLPARKRUN WROCŁAWM9.0M207.01995.000:16:3712.03.32333300:32:5711.03.26666700:49:5412.03.39000001:07:349.03.5333330.01506701:11:243.384688
910.0379DAMIANDYDUCHKĘPNOPOLAZS KU POLITECHNIKA OPOLSKAM10.0M302.01988.000:16:4214.03.34000000:32:5710.03.25000000:49:5411.03.39000001:07:3410.03.5333330.01440001:11:243.384688
MiejsceNumer startowyImięNazwiskoMiastoKrajDrużynaPłećPłeć MiejsceKategoria wiekowaKategoria wiekowa MiejsceRocznik5 km Czas5 km Miejsce Open5 km Tempo10 km Czas10 km Miejsce Open10 km Tempo15 km Czas15 km Miejsce Open15 km Tempo20 km Czas20 km Miejsce Open20 km TempoTempo StabilnośćCzasTempo
12997NaN23496RAFAŁŻULIŃSKINaNNaNNaNMNaNM40NaN1980.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12998NaN78027SEBASTIANŻURAWSKINaNNaNNaNMNaNM30NaN1988.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12999NaN21184KAMILŻUREKNaNNaN#2268MNaNM30NaN1985.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
13000NaN25626JAGODAŻUREKNaNNaNBrakKNaNK20NaN1995.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
13001NaN77701JANŻUREKNaNNaNBiegający EmerytMNaNM60NaN1960.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
13002NaN6445ANNAŻUROWSKANaNNaNNaNKNaNK40NaN1982.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
13003NaN23495JUSTYNAŻYGADŁONaNNaNNaNKNaNK20NaN1998.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
13004NaN9323DAWIDŻYTKOWSKINaNNaNNaNMNaNM20NaN1995.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDNSNaN
13005NaN27386DOMINIKAĆWIERTNIANaNNaNNaNKNaNK30NaN1991.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
13006NaN80025KRZYSZTOFĆWIĘKNaNNaNPkoMNaNM30NaN1988.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN